Gene selection tool (GST) : a R-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data

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dc.contributor.authorSugi Lee-
dc.contributor.authorMinah Jung-
dc.contributor.authorJaeeun Jung-
dc.contributor.authorKunhyang Park-
dc.contributor.authorJea Woon Ryu-
dc.contributor.authorJeongkil Kim-
dc.contributor.authorDae Soo Kim-
dc.date.accessioned2018-01-11-
dc.date.available2018-01-11-
dc.date.issued2017-
dc.identifier.issn1932-6203-
dc.identifier.uri10.1371/journal.pone.0185514ko
dc.identifier.urihttps://oak.kribb.re.kr/handle/201005/17454-
dc.description.abstractWhole-exome sequencing (WES) can identify causative mutations in hereditary diseases. However, WES data might have a large candidate variant list, including false positives. Moreover, in families, it is more difficult to select disease-associated variants because many variants are shared among members. To reduce false positives and extract accurate candidates, we used a multilocus variant instead of a single-locus variant (SNV). We set up a specific window to analyze the multilocus variant and devised a sliding-window approach to observe all variants. We developed the gene selection tool (GST) based on proportion tests for linkage analysis using WES data. This tool is R program coded and has high sensitivity. We tested our code to find the gene for hereditary spastic paraplegia using SNVs from a specific family and identified the gene known to cause the disease in a significant gene list. The list identified other genes that might be associated with the disease.-
dc.publisherPublic Library of Science-
dc.titleGene selection tool (GST) : a R-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data-
dc.title.alternativeGene selection tool (GST) : a R-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data-
dc.typeArticle-
dc.citation.titlePLoS One-
dc.citation.number9-
dc.citation.endPagee0185514-
dc.citation.startPagee0185514-
dc.citation.volume12-
dc.contributor.affiliatedAuthorSugi Lee-
dc.contributor.affiliatedAuthorMinah Jung-
dc.contributor.affiliatedAuthorJaeeun Jung-
dc.contributor.affiliatedAuthorKunhyang Park-
dc.contributor.affiliatedAuthorJea Woon Ryu-
dc.contributor.affiliatedAuthorJeongkil Kim-
dc.contributor.affiliatedAuthorDae Soo Kim-
dc.contributor.alternativeName이수기-
dc.contributor.alternativeName정민아-
dc.contributor.alternativeName정재은-
dc.contributor.alternativeName박근향-
dc.contributor.alternativeName유제운-
dc.contributor.alternativeName김정길-
dc.contributor.alternativeName김대수-
dc.identifier.bibliographicCitationPLoS One, vol. 12, no. 9, pp. e0185514-e0185514-
dc.identifier.doi10.1371/journal.pone.0185514-
dc.description.journalClassY-
Appears in Collections:
Division of A.I. & Biomedical Research > Digital Biotech Innovation Center > 1. Journal Articles
Division of Bio Technology Innovation > Core Research Facility & Analysis Center > 1. Journal Articles
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